Main objective:

- Contribution of supervised learning on simulated data in finance.

- The Chair is located at the meeting point between the increased calculation needs of investment banks, following the increase in regulation, and machine learning techniques that can be instrumental for this purpose.

Recent applications:

- Sensitivity calculation for CVA coverage and risk analysis

- Quantification and management of model risk in a hedging valuation adjustment XVA framework

- A fast neural regression and quantile regression algorithm for FVA and KVA calculations.

- Statistical learning of conditional value-at-risk and expected shortfall: A mathematical, algorithmic and numerical study.

- Quantitative analysis of the convergence of statistical approximation algorithms optimized for value-at-risk and expected shortfall

- Static coverage of multi-underlying derivatives by vanilla sneakers: mathematical study and numerical approaches using neural networks.

- Creation of a reference database for apprenticeships, for practitioners and academics in the field and beyond.

Teaching:

Participations in the XVA analysis course in M2MO and derivative products in M2ISIFAR (Paris Cité University).